Autonomous vehicles are widely used and include a variety of unmanned ground vehicles, underwater vehicles, and aerospace vehicles, such as robots and unmanned aerial vehicles (UAVs). An autonomous vehicle is required to make decisions and respond to situations completely without human intervention. There are major limitations to the overall performance, accuracy, and robustness of navigation and control of an autonomous vehicle. In order to perform navigation properly, an autonomous vehicle must be able to sense its location, steer toward a desired destination, and avoid obstacles. Various modalities have been used to provide navigation of autonomous vehicles. These include use of the Global Positioning System (GPS), inertial measurements from sensors, and image measurements from cameras.
Smaller UAVs are being developed for reconnaissance and surveillance that can be carried and deployed in the field by an individual or a small group. Such UAVs include micro air vehicles (MAVs) and organic air vehicles (OAVs), which can be remotely controlled. The typical dimension for MAVs is approximately six to twelve inches (15 to 30 cm), and development of insect-size MAVs is underway. Such air vehicles can be designed for operation in a battlefield by troops, and provide small combat teams and individual soldiers with the capability to detect enemy forces concealed in forests or hills, around buildings in urban areas, or in places where there is no direct line-of-sight. Some of these air vehicles can perch and stare, and essentially become sentinels for maneuvering troops.
In order to avoid obstacles during navigation, autonomous vehicles such as UAVs need three-dimensional (3-D) obstacle mapping. Typical vehicle sensors that are used currently are either very expensive (e.g., scanning laser detection and ranging (LADAR)) or require very computationally expensive algorithms (e.g., stereo cameras that try to track many features).